1 Load libraries and settings

suppressPackageStartupMessages(library(scMET))
suppressPackageStartupMessages(library(BASiCS))
suppressPackageStartupMessages(library(SingleCellExperiment))
suppressPackageStartupMessages(library(ggpubr))
suppressPackageStartupMessages(library(data.table))
suppressPackageStartupMessages(library(purrr))
suppressPackageStartupMessages(library(viridis))
suppressPackageStartupMessages(library(ggplot2))

#####################
## Define settings ##
#####################
source("../load_settings.R")
data_dir <- "/Users/ckapoura/datasets/scMET_ms/gastrulation/data/"
hits_dir <- "/Users/ckapoura/datasets/scMET_ms/gastrulation/metrna/hits/"
pdf_dir <- "/Users/ckapoura/datasets/scMET_ms/gastrulation/metrna/analysis/"
if (!dir.exists(hits_dir)) {dir.create(hits_dir, recursive = TRUE)}
if (!dir.exists(pdf_dir)) {dir.create(pdf_dir, recursive = TRUE)}

# Define genomic context
opts$anno <- c("prom_2000_2000")
#opts$anno <- c("first_intron")

2 RNA variability

2.1 Load BASiCS object and get posterior summary stats

# Load BASiCS object
basics_obj <- readRDS(file = paste0(data_dir, "/rna_basics.rds"))
# SingleCellExperiment object
sce <- readRDS(io$rna)[, sample_metadata$id_rna]
# Perform HVG analysis
HVG <- BASiCS_DetectVG(basics_obj, PercentileThreshold = 0.9,
                       EFDR = 0.01, Plot = FALSE)
Posterior probability threshold has been supplied 
EFDR will not be calibrated. 
hvg_results <- HVG@Table[, c("GeneName", "Prob", "HVG")]
colnames(hvg_results) <- c("ens_id", "rna_tail_prob", "rna_is_variable")

# Obtain posterior draws summary
chain_summary <- Summary(basics_obj)

# Merge Ensemb IDs with gene symbol
subset_meta <- rowData(sce[rownames(chain_summary@parameters$mu)]) %>%
  as.data.table(keep.rownames = "ens_id") %>%
  merge(., hvg_results, by = "ens_id")
Arguments in '...' ignored
# Get posterior medians summary
post_summary <- data.table(ens_id = rownames(chain_summary@parameters$mu),
                           rna_mu = log(chain_summary@parameters$mu[, 1]),
                           rna_epsilon = chain_summary@parameters$epsilon[, 1],
                           rna_delta = log(chain_summary@parameters$delta[, 1]) )

2.2 Plot RNA variability

2.2.1 Mean - overdispersion with trend line

# Show mean-overdispersion relationship
plot(BASiCS_ShowFit(basics_obj))

2.2.2 Mean - overdispersion with HVG analysis

# Combine data 
joint_rna_dt <- merge(subset_meta, post_summary, by = "ens_id") %>%
  setorder(-rna_epsilon)

to.plot <- joint_rna_dt[rna_mu > 0.1]
tmp <- to.plot[to.plot$rna_is_variable == FALSE, ]
tmp <- tmp[sample(NROW(tmp), 2000), ]
to.plot <- rbind(to.plot[to.plot$rna_is_variable == TRUE, ], tmp)

mode <- c("rna_delta", "rna_epsilon") 
y_lab <- list(
  "rna_delta" = "RNA log overdispersion",
  "rna_epsilon" = "RNA residual overdispersion"
)
for (m in mode) {
  gg <- ggplot(to.plot, aes_string(x = "rna_mu", y = m)) +
    geom_point(aes(fill = rna_is_variable, alpha = rna_is_variable, size = rna_is_variable),
               shape = 21, stroke = 0.01) +
    scale_fill_manual(values = c("gray80","red")) +
    scale_alpha_manual(values = c(0.4, 0.6)) +
    scale_size_manual(values = c(0.65, 1.6)) +
    guides(fill = guide_legend(override.aes = list(size = 2.5))) +
    xlab("Log mean expression") + ylab(y_lab[[m]]) +
    ggrepel::geom_text_repel(data = head(to.plot[rna_is_variable == TRUE], n = 20),
                             aes_string(x = "rna_mu", y = m, label = "symbol"),
                             size = 3, color = "black") +
    theme_classic() +
    theme(
      legend.position = "none",
      strip.text = element_text(color = "black", size = rel(1.1)),
      legend.text = element_text(color = "black", size = rel(1.15)),
      axis.text = element_text(color = "black", size = rel(1)),
      axis.title = element_text(color = "black", size = rel(1.2))
    )
  print(gg)
  pdf(file = paste0(pdf_dir, "basics_meanvar_", m, ".pdf"), 
      width = 7, height = 5, useDingbats = FALSE)
  print(gg)
  dev.off()
}

2.2.3 Store HVG results

rna_hvg <- joint_rna_dt[, c("ens_id", "symbol", "rna_tail_prob", "rna_is_variable",
                  "rna_mu", "rna_epsilon", "rna_delta")] %>%
  setnames(c("symbol"), c("rna_symbol"))
fwrite(rna_hvg, paste0(hits_dir,"/rna_hvg.txt.gz"), sep = "\t", quote = FALSE)

3 DNAm variability

# Perform HVF analysis
obj <- readRDS(sprintf("%s/%s_vb.rds", data_dir, opts$anno))
scmet_obj <- scmet_hvf(scmet_obj = obj, delta_e = 0.9, delta_g = NULL, efdr = 0.1)
For HVF detection task:
Evidence probability threshold chosen via EFDR valibration is too low. 
Probability threshold set automatically equal to 'evidence_thresh'.

641 Features classified as highly variable using: 
- The  90 percentile of variable genes 
- Evidence threshold = 0.8
- EFDR = 6.74% 
- EFNR = 9.78% 
# Obtain gene names
gene_metadata <- fread(io$gene.metadata) %>% .[,c("ens_id", "symbol")]
#Highlight top hits for promoters
met_joint <- scmet_obj$hvf$summary %>% as.data.table %>%
  setnames("feature_name", "ens_id") %>%
  merge(gene_metadata, by = "ens_id") %>%
  setorder(-epsilon)

3.1 Mean - overdispersion with HVF analysis

mode <- c("epsilon", "gamma")
for (m in mode) {
  
  gg <- scmet_plot_mean_var(obj = scmet_obj, y = m, task = "hvf",
                            nfeatures = 3000) +
    theme(legend.position = c(0.9, 0.1)) +
  ggrepel::geom_text_repel(data = head(met_joint, n = 35),
                           aes_string(x = "mu", y = m, label = "symbol"),
                           size = 2.3, color = "black")
  print(gg)
  pdf(paste0(pdf_dir, "/scmet_meanvar_", opts$anno, "_", m, ".pdf"),
      width = 7, height = 5, useDingbats = FALSE)
  print(gg)
  dev.off()
}

3.2 Store HVF analysis results

met_hvf <- scmet_obj$hvf$summary %>%
  as.data.table %>% .[, anno := opts$anno] %>%
  setnames("feature_name", "ens_id")
fwrite(met_hvf, paste0(hits_dir, "/met_hvf_", opts$anno, ".txt.gz"), sep = "\t", quote = FALSE)

4 Joint analysis

## Combine omics
metrna <- merge(met_hvf, rna_hvg, by = "ens_id") %>% 
  merge(gene_metadata, by = "ens_id")

# Process
metrna[, color := "black"]
metrna[epsilon > 0.5 & rna_epsilon > 1.5, color := "red"]
metrna[epsilon < 0.15 & rna_epsilon > 2, color := "blue"]
metrna <- metrna %>% setorder(-rna_epsilon)
fwrite(metrna, file = paste0(hits_dir, "metrna_variability_", opts$anno, ".csv"), 
       sep = "\t", quote = FALSE)

4.1 Plot RNA and DNA co-variability

set.seed(12)
# Marker genes
marker_genes <- c(
  "Cubn", "Cer1", "Mixl1", "Lefty2", "Amn", "Cldn6", "Mesp1",
  "Krt8", "Apob", "Foxi1", "Aplnr", "Id3", "Peg3",  "Dppa5a", "Dppa4",
  "Dppa2", "Spp1", "Morc1", "Trap1a", "Pcdh19", "Zfp42", "Fmr1nb"
)
if (opts$anno == "prom_2000_2000") {
  main_txt <- "Promoter region"
} else if (opts$anno == "first_exon") {
  main_txt <- "First exon" 
} else {
  main_txt <- "First intron"
}
#main_txt <- ifelse(opts$anno == "prom_2000_2000", "Promoter region", "First exon")
# Create copy
metrna_plot <- copy(metrna)
tmp <- metrna_plot[metrna_plot$color == "black", ]
tmp <- tmp[sample(NROW(tmp), ifelse(opts$anno == "prom_2000_2000", 4000, 1000)), ]
to.plot <- rbind(metrna_plot[metrna_plot$color != "black", ], tmp)
gg <- ggplot(to.plot, aes(x = epsilon, y = rna_epsilon, 
                        alpha = color, fill = color, size = color)) +
  labs(x = expression(paste("Residual overdispersion (DNAm)")),
       y = "Residual overdispersion (RNA)") +
  geom_point(shape = 21, stroke = 0.1, color = "black") +
  scale_fill_manual(values = c("black" = "gray80", "red" = "#4E934D", "blue" = "#C400AD")) +
  scale_size_manual(values = c("black" = 0.6, "red" = 2, "blue" = 2)) +
  scale_alpha_manual(values = c("black" = 0.5, "red" = 0.8, "blue" = 0.8)) +
  ggrepel::geom_text_repel(data = to.plot[color == "red" & symbol %in% marker_genes],
                           aes(x = epsilon, y = rna_epsilon, label = symbol),
                           size = 5, color = "#4E934D") +
  ggrepel::geom_text_repel(data = to.plot[color == "blue" & symbol %in% marker_genes],
                           aes(x = epsilon,  y = rna_epsilon, label = symbol),
                           size = 5, color = "#C400AD") +
  coord_cartesian(xlim = c(-0.9, 1.6)) +
  theme_classic() +
  ggtitle(main_txt) +
  theme(
    legend.position = "none",
    plot.title = element_text(hjust = 0.5, face = "bold", color = "black", size = rel(1.45)),
    strip.text = element_text(color = "black", size = rel(1.1)),
    legend.text = element_text(color = "black", size = rel(1.15)),
    axis.text = element_text(color = "black", size = rel(1.05)),
    axis.title = element_text(color = "black", size = rel(1.3))
  )
print(gg)

pdf(paste0(pdf_dir, "/metrna_variability_", opts$anno, ".pdf"), width = 10, height = 4, useDingbats = FALSE)
print(gg)
dev.off()
quartz_off_screen 
                2 

4.2 Plot RNA and DNA mean levels

set.seed(12)
# Marker genes
marker_genes <- c(
  "Cubn", "Cer1", "Mixl1", "Lefty2", "Amn", "Cldn6", "Mesp1",
  "Krt8", "Apob", "Foxi1", "Aplnr", "Id3", "Peg3",  "Dppa5a", "Dppa4",
  "Dppa2", "Spp1", "Morc1", "Trap1a", "Pcdh19", "Zfp42", "Fmr1nb"
)

#main_txt <- ifelse(opts$anno == "prom_2000_2000", "Promoter region", "First exon")
if (opts$anno == "prom_2000_2000") {
  main_txt <- "Promoter region"
} else if (opts$anno == "first_exon") {
  main_txt <- "First exon" 
} else {
  main_txt <- "First intron"
}
# Create copy
metrna_plot <- copy(metrna)
tmp <- metrna_plot[metrna_plot$color == "black", ]
tmp <- tmp[sample(NROW(tmp), ifelse(opts$anno == "prom_2000_2000", 4000, 1000)), ]
to.plot <- rbind(metrna_plot[metrna_plot$color != "black", ], tmp)
gg <- ggplot(to.plot, aes(x = mu, y = rna_mu, 
                        alpha = color, fill = color, size = color)) +
  labs(x = expression(paste("Mean DNA methylation")),
       y = "Mean RNA expression") +
  geom_point(shape = 21, stroke = 0.1, color = "black") +
  scale_fill_manual(values = c("black" = "gray80", "red" = "#4E934D", "blue" = "#C400AD")) +
  scale_size_manual(values = c("black" = 0.6, "red" = 2, "blue" = 2)) +
  scale_alpha_manual(values = c("black" = 0.5, "red" = 0.8, "blue" = 0.8)) +
  ggrepel::geom_text_repel(data = to.plot[color == "red" & symbol %in% marker_genes],
                           aes(x = mu, y = rna_mu, label = symbol),
                           size = 5, color = "#4E934D") +
  ggrepel::geom_text_repel(data = to.plot[color == "blue" & symbol %in% marker_genes],
                           aes(x = mu,  y = rna_mu, label = symbol),
                           size = 5, color = "#C400AD") +
  annotate("text", y = 9, x = 0.88,label="R = -0.4",hjust = 1) +
  #geom_smooth(method=lm, se=FALSE) +
  #coord_cartesian(xlim = c(, 1.6)) +
  theme_classic() +
  ggtitle(main_txt) +
  theme(
    legend.position = "none",
    plot.title = element_text(hjust = 0.5, face = "bold", color = "black", size = rel(1.45)),
    strip.text = element_text(color = "black", size = rel(1.1)),
    legend.text = element_text(color = "black", size = rel(1.15)),
    axis.text = element_text(color = "black", size = rel(1.05)),
    axis.title = element_text(color = "black", size = rel(1.3))
  )
print(gg)

pdf(paste0(pdf_dir, "/metrna_mean_", opts$anno, ".pdf"), width = 8, height = 5, useDingbats = FALSE)
print(gg)
dev.off()
quartz_off_screen 
                2 

4.3 Plot RNA and DNA mean levels

set.seed(12)
# Get density of points in 2 dimensions.
# @param x A numeric vector.
# @param y A numeric vector.
# @param n Create a square n by n grid to compute density.
# @return The density within each square.
.get_density <- function(x, y, ...) {
  dens <- MASS::kde2d(x, y, ...)
  ix <- findInterval(x, dens$x)
  iy <- findInterval(y, dens$y)
  ii <- cbind(ix, iy)
  return(dens$z[ii])
}

# ggplot(parameter_df[ind_not_na, ], aes(log10(cv2_scran), log10(delta))) + geom_pointdensity() + scale_colour_viridis(name = "Density")
# library("ggpointdensity")
# library("viridis")

# Marker genes
marker_genes <- c(
  "Cubn", "Cer1", "Mixl1", "Lefty2", "Amn", "Cldn6", "Mesp1",
  "Krt8", "Apob", "Foxi1", "Aplnr", "Id3", "Peg3",  "Dppa5a", "Dppa4",
  "Dppa2", "Spp1", "Morc1", "Trap1a", "Pcdh19", "Zfp42", "Fmr1nb"
)

main_txt <- ifelse(opts$anno == "prom_2000_2000", "Promoter region", "First exon")
# Create copy
metrna_plot <- copy(metrna)
tmp <- metrna_plot[metrna_plot$color == "black", ]
#tmp <- tmp[sample(NROW(tmp), ifelse(opts$anno == "prom_2000_2000", 4000, 1000)), ]
to.plot <- metrna_plot# rbind(metrna_plot[metrna_plot$color != "black", ], tmp)

to.plot <- to.plot %>% .[, density := .get_density(log(mu), rna_mu, n = 50)]
gg <- ggplot(to.plot, aes(x = log(mu), y = rna_mu, color = density)) +
  labs(x = expression(paste("Log mean DNA methylation")),
       y = "Log mean RNA expression") +
  geom_point(size = 1) +
  viridis::scale_fill_viridis() +
  viridis::scale_color_viridis() +
  # ggrepel::geom_text_repel(data = to.plot[color == "red" & symbol %in% marker_genes],
  #                          aes(x = log(mu), y = rna_mu, label = symbol),
  #                          size = 5, color = "#4E934D") +
  # ggrepel::geom_text_repel(data = to.plot[color == "blue" & symbol %in% marker_genes],
  #                          aes(x = log(mu),  y = rna_mu, label = symbol),
  #                          size = 5, color = "#C400AD") +
  #annotate("text", y = 9, x = 0.88,label="R = -0.4",hjust = 1) +
  annotate("text", y = 9, x = 0,label="R = -0.4",hjust = 1) +
  #geom_smooth(method=lm, se=FALSE) +
  #coord_cartesian(xlim = c(, 1.6)) +
  theme_classic() +
  ggtitle(main_txt) +
  theme(
    legend.position = "none",
    plot.title = element_text(hjust = 0.5, face = "bold", color = "black", size = rel(1.45)),
    strip.text = element_text(color = "black", size = rel(1.1)),
    legend.text = element_text(color = "black", size = rel(1.15)),
    axis.text = element_text(color = "black", size = rel(1.05)),
    axis.title = element_text(color = "black", size = rel(1.3))
  )
print(gg)

pdf(paste0(pdf_dir, "/metrna_mean_heatmap_", opts$anno, ".pdf"), width = 8, height = 5, useDingbats = FALSE)
print(gg)
dev.off()
quartz_off_screen 
                2 

---
title: "scNMT-seq gastrulation analysis"
author: "C.A.Kapourani"
output: 
  html_notebook:
    df_print: paged
    highlight: haddock
    number_sections: yes
    theme: cerulean
    toc: yes
---

```{r set_global_options, cache=FALSE, results='hide', echo=FALSE, warning=FALSE, message=FALSE}
library('knitr')
knitr::opts_chunk$set(dpi = 75, warning = FALSE, message = FALSE)
```

# Load libraries and settings
```{r}
suppressPackageStartupMessages(library(scMET))
suppressPackageStartupMessages(library(BASiCS))
suppressPackageStartupMessages(library(SingleCellExperiment))
suppressPackageStartupMessages(library(ggpubr))
suppressPackageStartupMessages(library(data.table))
suppressPackageStartupMessages(library(purrr))
suppressPackageStartupMessages(library(viridis))
suppressPackageStartupMessages(library(ggplot2))

#####################
## Define settings ##
#####################
source("../load_settings.R")
data_dir <- "/Users/ckapoura/datasets/scMET_ms/gastrulation/data/"
hits_dir <- "/Users/ckapoura/datasets/scMET_ms/gastrulation/metrna/hits/"
pdf_dir <- "/Users/ckapoura/datasets/scMET_ms/gastrulation/metrna/analysis/"
if (!dir.exists(hits_dir)) {dir.create(hits_dir, recursive = TRUE)}
if (!dir.exists(pdf_dir)) {dir.create(pdf_dir, recursive = TRUE)}

# Define genomic context
opts$anno <- c("prom_2000_2000")
#opts$anno <- c("first_intron")
```

# RNA variability
## Load BASiCS object and get posterior summary stats
```{r}
# Load BASiCS object
basics_obj <- readRDS(file = paste0(data_dir, "/rna_basics.rds"))
# SingleCellExperiment object
sce <- readRDS(io$rna)[, sample_metadata$id_rna]
# Perform HVG analysis
HVG <- BASiCS_DetectVG(basics_obj, PercentileThreshold = 0.9,
                       EFDR = 0.01, Plot = FALSE)
hvg_results <- HVG@Table[, c("GeneName", "Prob", "HVG")]
colnames(hvg_results) <- c("ens_id", "rna_tail_prob", "rna_is_variable")

# Obtain posterior draws summary
chain_summary <- Summary(basics_obj)

# Merge Ensemb IDs with gene symbol
subset_meta <- rowData(sce[rownames(chain_summary@parameters$mu)]) %>%
  as.data.table(keep.rownames = "ens_id") %>%
  merge(., hvg_results, by = "ens_id")

# Get posterior medians summary
post_summary <- data.table(ens_id = rownames(chain_summary@parameters$mu),
                           rna_mu = log(chain_summary@parameters$mu[, 1]),
                           rna_epsilon = chain_summary@parameters$epsilon[, 1],
                           rna_delta = log(chain_summary@parameters$delta[, 1]) )
```

## Plot RNA variability
### Mean - overdispersion with trend line
```{r}
# Show mean-overdispersion relationship
plot(BASiCS_ShowFit(basics_obj))
```

### Mean - overdispersion with HVG analysis
```{r, fig.width=3.2, fig.height=2.5, message=FALSE, warning=FALSE}
# Combine data 
joint_rna_dt <- merge(subset_meta, post_summary, by = "ens_id") %>%
  setorder(-rna_epsilon)

to.plot <- joint_rna_dt[rna_mu > 0.1]
tmp <- to.plot[to.plot$rna_is_variable == FALSE, ]
tmp <- tmp[sample(NROW(tmp), 2000), ]
to.plot <- rbind(to.plot[to.plot$rna_is_variable == TRUE, ], tmp)

mode <- c("rna_delta", "rna_epsilon") 
y_lab <- list(
  "rna_delta" = "RNA log overdispersion",
  "rna_epsilon" = "RNA residual overdispersion"
)
for (m in mode) {
  gg <- ggplot(to.plot, aes_string(x = "rna_mu", y = m)) +
    geom_point(aes(fill = rna_is_variable, alpha = rna_is_variable, size = rna_is_variable),
               shape = 21, stroke = 0.01) +
    scale_fill_manual(values = c("gray80","red")) +
    scale_alpha_manual(values = c(0.4, 0.6)) +
    scale_size_manual(values = c(0.65, 1.6)) +
    guides(fill = guide_legend(override.aes = list(size = 2.5))) +
    xlab("Log mean expression") + ylab(y_lab[[m]]) +
    ggrepel::geom_text_repel(data = head(to.plot[rna_is_variable == TRUE], n = 20),
                             aes_string(x = "rna_mu", y = m, label = "symbol"),
                             size = 3, color = "black") +
    theme_classic() +
    theme(
      legend.position = "none",
      strip.text = element_text(color = "black", size = rel(1.1)),
      legend.text = element_text(color = "black", size = rel(1.15)),
      axis.text = element_text(color = "black", size = rel(1)),
      axis.title = element_text(color = "black", size = rel(1.2))
    )
  print(gg)
  pdf(file = paste0(pdf_dir, "basics_meanvar_", m, ".pdf"), 
      width = 7, height = 5, useDingbats = FALSE)
  print(gg)
  dev.off()
}
```

### Store HVG results
```{r}
rna_hvg <- joint_rna_dt[, c("ens_id", "symbol", "rna_tail_prob", "rna_is_variable",
                  "rna_mu", "rna_epsilon", "rna_delta")] %>%
  setnames(c("symbol"), c("rna_symbol"))
fwrite(rna_hvg, paste0(hits_dir,"/rna_hvg.txt.gz"), sep = "\t", quote = FALSE)
```


# DNAm variability
```{r}
# Perform HVF analysis
obj <- readRDS(sprintf("%s/%s_vb.rds", data_dir, opts$anno))
scmet_obj <- scmet_hvf(scmet_obj = obj, delta_e = 0.9, delta_g = NULL, efdr = 0.1)

# Obtain gene names
gene_metadata <- fread(io$gene.metadata) %>% .[,c("ens_id", "symbol")]
#Highlight top hits for promoters
met_joint <- scmet_obj$hvf$summary %>% as.data.table %>%
  setnames("feature_name", "ens_id") %>%
  merge(gene_metadata, by = "ens_id") %>%
  setorder(-epsilon)
```


## Mean - overdispersion with HVF analysis
```{r}
mode <- c("epsilon", "gamma")
for (m in mode) {
  
  gg <- scmet_plot_mean_var(obj = scmet_obj, y = m, task = "hvf",
                            nfeatures = 3000) +
    theme(legend.position = c(0.9, 0.1)) +
  ggrepel::geom_text_repel(data = head(met_joint, n = 35),
                           aes_string(x = "mu", y = m, label = "symbol"),
                           size = 2.3, color = "black")
  print(gg)
  pdf(paste0(pdf_dir, "/scmet_meanvar_", opts$anno, "_", m, ".pdf"),
      width = 7, height = 5, useDingbats = FALSE)
  print(gg)
  dev.off()
}
```


## Store HVF analysis results
```{r}
met_hvf <- scmet_obj$hvf$summary %>%
  as.data.table %>% .[, anno := opts$anno] %>%
  setnames("feature_name", "ens_id")
fwrite(met_hvf, paste0(hits_dir, "/met_hvf_", opts$anno, ".txt.gz"), sep = "\t", quote = FALSE)
```

# Joint analysis
```{r}
## Combine omics
metrna <- merge(met_hvf, rna_hvg, by = "ens_id") %>% 
  merge(gene_metadata, by = "ens_id")

# Process
metrna[, color := "black"]
metrna[epsilon > 0.5 & rna_epsilon > 1.5, color := "red"]
metrna[epsilon < 0.15 & rna_epsilon > 2, color := "blue"]
metrna <- metrna %>% setorder(-rna_epsilon)
fwrite(metrna, file = paste0(hits_dir, "metrna_variability_", opts$anno, ".csv"), 
       sep = "\t", quote = FALSE)
```


## Plot RNA and DNA co-variability
```{r, warning=FALSE, message=FALSE}
set.seed(12)
# Marker genes
marker_genes <- c(
  "Cubn", "Cer1", "Mixl1", "Lefty2", "Amn", "Cldn6", "Mesp1",
  "Krt8", "Apob", "Foxi1", "Aplnr", "Id3", "Peg3",  "Dppa5a", "Dppa4",
  "Dppa2", "Spp1", "Morc1", "Trap1a", "Pcdh19", "Zfp42", "Fmr1nb"
)
if (opts$anno == "prom_2000_2000") {
  main_txt <- "Promoter region"
} else if (opts$anno == "first_exon") {
  main_txt <- "First exon" 
} else {
  main_txt <- "First intron"
}
#main_txt <- ifelse(opts$anno == "prom_2000_2000", "Promoter region", "First exon")
# Create copy
metrna_plot <- copy(metrna)
tmp <- metrna_plot[metrna_plot$color == "black", ]
tmp <- tmp[sample(NROW(tmp), ifelse(opts$anno == "prom_2000_2000", 4000, 1000)), ]
to.plot <- rbind(metrna_plot[metrna_plot$color != "black", ], tmp)
gg <- ggplot(to.plot, aes(x = epsilon, y = rna_epsilon, 
                        alpha = color, fill = color, size = color)) +
  labs(x = expression(paste("Residual overdispersion (DNAm)")),
       y = "Residual overdispersion (RNA)") +
  geom_point(shape = 21, stroke = 0.1, color = "black") +
  scale_fill_manual(values = c("black" = "gray80", "red" = "#4E934D", "blue" = "#C400AD")) +
  scale_size_manual(values = c("black" = 0.6, "red" = 2, "blue" = 2)) +
  scale_alpha_manual(values = c("black" = 0.5, "red" = 0.8, "blue" = 0.8)) +
  ggrepel::geom_text_repel(data = to.plot[color == "red" & symbol %in% marker_genes],
                           aes(x = epsilon, y = rna_epsilon, label = symbol),
                           size = 5, color = "#4E934D") +
  ggrepel::geom_text_repel(data = to.plot[color == "blue" & symbol %in% marker_genes],
                           aes(x = epsilon,  y = rna_epsilon, label = symbol),
                           size = 5, color = "#C400AD") +
  coord_cartesian(xlim = c(-0.9, 1.6)) +
  theme_classic() +
  ggtitle(main_txt) +
  theme(
    legend.position = "none",
    plot.title = element_text(hjust = 0.5, face = "bold", color = "black", size = rel(1.45)),
    strip.text = element_text(color = "black", size = rel(1.1)),
    legend.text = element_text(color = "black", size = rel(1.15)),
    axis.text = element_text(color = "black", size = rel(1.05)),
    axis.title = element_text(color = "black", size = rel(1.3))
  )
print(gg)

pdf(paste0(pdf_dir, "/metrna_variability_", opts$anno, ".pdf"), width = 10, height = 4, useDingbats = FALSE)
print(gg)
dev.off()
```

## Plot RNA and DNA mean levels
```{r, warning=FALSE, message=FALSE}
set.seed(12)
# Marker genes
marker_genes <- c(
  "Cubn", "Cer1", "Mixl1", "Lefty2", "Amn", "Cldn6", "Mesp1",
  "Krt8", "Apob", "Foxi1", "Aplnr", "Id3", "Peg3",  "Dppa5a", "Dppa4",
  "Dppa2", "Spp1", "Morc1", "Trap1a", "Pcdh19", "Zfp42", "Fmr1nb"
)

#main_txt <- ifelse(opts$anno == "prom_2000_2000", "Promoter region", "First exon")
if (opts$anno == "prom_2000_2000") {
  main_txt <- "Promoter region"
} else if (opts$anno == "first_exon") {
  main_txt <- "First exon" 
} else {
  main_txt <- "First intron"
}
# Create copy
metrna_plot <- copy(metrna)
tmp <- metrna_plot[metrna_plot$color == "black", ]
tmp <- tmp[sample(NROW(tmp), ifelse(opts$anno == "prom_2000_2000", 4000, 1000)), ]
to.plot <- rbind(metrna_plot[metrna_plot$color != "black", ], tmp)
gg <- ggplot(to.plot, aes(x = mu, y = rna_mu, 
                        alpha = color, fill = color, size = color)) +
  labs(x = expression(paste("Mean DNA methylation")),
       y = "Mean RNA expression") +
  geom_point(shape = 21, stroke = 0.1, color = "black") +
  scale_fill_manual(values = c("black" = "gray80", "red" = "#4E934D", "blue" = "#C400AD")) +
  scale_size_manual(values = c("black" = 0.6, "red" = 2, "blue" = 2)) +
  scale_alpha_manual(values = c("black" = 0.5, "red" = 0.8, "blue" = 0.8)) +
  ggrepel::geom_text_repel(data = to.plot[color == "red" & symbol %in% marker_genes],
                           aes(x = mu, y = rna_mu, label = symbol),
                           size = 5, color = "#4E934D") +
  ggrepel::geom_text_repel(data = to.plot[color == "blue" & symbol %in% marker_genes],
                           aes(x = mu,  y = rna_mu, label = symbol),
                           size = 5, color = "#C400AD") +
  annotate("text", y = 9, x = 0.88,label="R = -0.4",hjust = 1) +
  #geom_smooth(method=lm, se=FALSE) +
  #coord_cartesian(xlim = c(, 1.6)) +
  theme_classic() +
  ggtitle(main_txt) +
  theme(
    legend.position = "none",
    plot.title = element_text(hjust = 0.5, face = "bold", color = "black", size = rel(1.45)),
    strip.text = element_text(color = "black", size = rel(1.1)),
    legend.text = element_text(color = "black", size = rel(1.15)),
    axis.text = element_text(color = "black", size = rel(1.05)),
    axis.title = element_text(color = "black", size = rel(1.3))
  )
print(gg)

pdf(paste0(pdf_dir, "/metrna_mean_", opts$anno, ".pdf"), width = 8, height = 5, useDingbats = FALSE)
print(gg)
dev.off()
```


## Plot RNA and DNA mean levels
```{r, warning=FALSE, message=FALSE}
set.seed(12)
# Get density of points in 2 dimensions.
# @param x A numeric vector.
# @param y A numeric vector.
# @param n Create a square n by n grid to compute density.
# @return The density within each square.
.get_density <- function(x, y, ...) {
  dens <- MASS::kde2d(x, y, ...)
  ix <- findInterval(x, dens$x)
  iy <- findInterval(y, dens$y)
  ii <- cbind(ix, iy)
  return(dens$z[ii])
}

# ggplot(parameter_df[ind_not_na, ], aes(log10(cv2_scran), log10(delta))) + geom_pointdensity() + scale_colour_viridis(name = "Density")
# library("ggpointdensity")
# library("viridis")

# Marker genes
marker_genes <- c(
  "Cubn", "Cer1", "Mixl1", "Lefty2", "Amn", "Cldn6", "Mesp1",
  "Krt8", "Apob", "Foxi1", "Aplnr", "Id3", "Peg3",  "Dppa5a", "Dppa4",
  "Dppa2", "Spp1", "Morc1", "Trap1a", "Pcdh19", "Zfp42", "Fmr1nb"
)

main_txt <- ifelse(opts$anno == "prom_2000_2000", "Promoter region", "First exon")
# Create copy
metrna_plot <- copy(metrna)
tmp <- metrna_plot[metrna_plot$color == "black", ]
#tmp <- tmp[sample(NROW(tmp), ifelse(opts$anno == "prom_2000_2000", 4000, 1000)), ]
to.plot <- metrna_plot# rbind(metrna_plot[metrna_plot$color != "black", ], tmp)

to.plot <- to.plot %>% .[, density := .get_density(log(mu), rna_mu, n = 50)]
gg <- ggplot(to.plot, aes(x = log(mu), y = rna_mu, color = density)) +
  labs(x = expression(paste("Log mean DNA methylation")),
       y = "Log mean RNA expression") +
  geom_point(size = 1) +
  viridis::scale_fill_viridis() +
  viridis::scale_color_viridis() +
  # ggrepel::geom_text_repel(data = to.plot[color == "red" & symbol %in% marker_genes],
  #                          aes(x = log(mu), y = rna_mu, label = symbol),
  #                          size = 5, color = "#4E934D") +
  # ggrepel::geom_text_repel(data = to.plot[color == "blue" & symbol %in% marker_genes],
  #                          aes(x = log(mu),  y = rna_mu, label = symbol),
  #                          size = 5, color = "#C400AD") +
  #annotate("text", y = 9, x = 0.88,label="R = -0.4",hjust = 1) +
  annotate("text", y = 9, x = 0,label="R = -0.4",hjust = 1) +
  #geom_smooth(method=lm, se=FALSE) +
  #coord_cartesian(xlim = c(, 1.6)) +
  theme_classic() +
  ggtitle(main_txt) +
  theme(
    legend.position = "none",
    plot.title = element_text(hjust = 0.5, face = "bold", color = "black", size = rel(1.45)),
    strip.text = element_text(color = "black", size = rel(1.1)),
    legend.text = element_text(color = "black", size = rel(1.15)),
    axis.text = element_text(color = "black", size = rel(1.05)),
    axis.title = element_text(color = "black", size = rel(1.3))
  )
print(gg)

pdf(paste0(pdf_dir, "/metrna_mean_heatmap_", opts$anno, ".pdf"), width = 8, height = 5, useDingbats = FALSE)
print(gg)
dev.off()
```

